本文整理汇总了Python中mlflow.get_artifact_uri方法的典型用法代码示例。如果您正苦于以下问题:Python mlflow.get_artifact_uri方法的具体用法?Python mlflow.get_artifact_uri怎么用?Python mlflow.get_artifact_uri使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mlflow
的用法示例。
在下文中一共展示了mlflow.get_artifact_uri方法的11个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_artifact_can_be_downloaded_from_absolute_uri_successfully
# 需要导入模块: import mlflow [as 别名]
# 或者: from mlflow import get_artifact_uri [as 别名]
def test_artifact_can_be_downloaded_from_absolute_uri_successfully(tmpdir):
artifact_file_name = "artifact.txt"
artifact_text = "Sample artifact text"
local_artifact_path = tmpdir.join(artifact_file_name).strpath
with open(local_artifact_path, "w") as out:
out.write(artifact_text)
logged_artifact_path = "artifact"
with mlflow.start_run():
mlflow.log_artifact(local_path=local_artifact_path, artifact_path=logged_artifact_path)
artifact_uri = mlflow.get_artifact_uri(artifact_path=logged_artifact_path)
downloaded_artifact_path = os.path.join(
_download_artifact_from_uri(artifact_uri), artifact_file_name)
assert downloaded_artifact_path != local_artifact_path
assert downloaded_artifact_path != logged_artifact_path
with open(downloaded_artifact_path, "r") as f:
assert f.read() == artifact_text
示例2: test_download_artifact_from_absolute_uri_persists_data_to_specified_output_directory
# 需要导入模块: import mlflow [as 别名]
# 或者: from mlflow import get_artifact_uri [as 别名]
def test_download_artifact_from_absolute_uri_persists_data_to_specified_output_directory(tmpdir):
artifact_file_name = "artifact.txt"
artifact_text = "Sample artifact text"
local_artifact_path = tmpdir.join(artifact_file_name).strpath
with open(local_artifact_path, "w") as out:
out.write(artifact_text)
logged_artifact_subdir = "logged_artifact"
with mlflow.start_run():
mlflow.log_artifact(local_path=local_artifact_path, artifact_path=logged_artifact_subdir)
artifact_uri = mlflow.get_artifact_uri(artifact_path=logged_artifact_subdir)
artifact_output_path = tmpdir.join("artifact_output").strpath
os.makedirs(artifact_output_path)
_download_artifact_from_uri(artifact_uri=artifact_uri, output_path=artifact_output_path)
assert logged_artifact_subdir in os.listdir(artifact_output_path)
assert artifact_file_name in os.listdir(
os.path.join(artifact_output_path, logged_artifact_subdir))
with open(os.path.join(
artifact_output_path, logged_artifact_subdir, artifact_file_name), "r") as f:
assert f.read() == artifact_text
示例3: log_saved_model
# 需要导入模块: import mlflow [as 别名]
# 或者: from mlflow import get_artifact_uri [as 别名]
def log_saved_model(saved_model_path: Union[bytes, str],
global_step: int,
saved_model_load_fn: Callable):
"""
Log all the saved models to mlflow
Parameters
----------
saved_model_path
path to saved model
global_step
global step for saved model
"""
# pylint: disable=unused-argument
# saved_model_load_fn is coming from patch
if mlflow.active_run() is None:
_warn_about_no_run()
return
if isinstance(saved_model_path, bytes):
saved_model_path = saved_model_path.decode()
saved_model_tag = os.path.split(saved_model_path)[-1]
artifact_path = os.path.join("models", saved_model_tag)
mlflow_tf.log_model(
tf_saved_model_dir=saved_model_path,
tf_meta_graph_tags=[tag_constants.SERVING],
tf_signature_def_key=
signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY,
artifact_path=artifact_path)
mlflow_artifacts_path = mlflow.get_artifact_uri()
saved_model_artifact_path = os.path.join(
mlflow_artifacts_path, artifact_path)
project_utils.log_exported_model_info(
saved_model_artifact_path, global_step)
示例4: get_artifact_uri
# 需要导入模块: import mlflow [as 别名]
# 或者: from mlflow import get_artifact_uri [as 别名]
def get_artifact_uri():
return None
示例5: test_log_artifact_with_dirs
# 需要导入模块: import mlflow [as 别名]
# 或者: from mlflow import get_artifact_uri [as 别名]
def test_log_artifact_with_dirs(tmpdir):
# Test log artifact with a directory
art_dir = tmpdir.mkdir("parent")
file0 = art_dir.join("file0")
file0.write("something")
file1 = art_dir.join("file1")
file1.write("something")
sub_dir = art_dir.mkdir("child")
with start_run():
artifact_uri = mlflow.get_artifact_uri()
run_artifact_dir = local_file_uri_to_path(artifact_uri)
mlflow.log_artifact(str(art_dir))
base = os.path.basename(str(art_dir))
assert os.listdir(run_artifact_dir) == [base]
assert set(os.listdir(os.path.join(run_artifact_dir, base))) == \
{'child', 'file0', 'file1'}
with open(os.path.join(run_artifact_dir, base, "file0")) as f:
assert f.read() == "something"
# Test log artifact with directory and specified parent folder
art_dir = tmpdir.mkdir("dir")
with start_run():
artifact_uri = mlflow.get_artifact_uri()
run_artifact_dir = local_file_uri_to_path(artifact_uri)
mlflow.log_artifact(str(art_dir), "some_parent")
assert os.listdir(run_artifact_dir) == [os.path.basename("some_parent")]
assert os.listdir(os.path.join(run_artifact_dir, "some_parent")) == \
[os.path.basename(str(art_dir))]
sub_dir = art_dir.mkdir("another_dir")
with start_run():
artifact_uri = mlflow.get_artifact_uri()
run_artifact_dir = local_file_uri_to_path(artifact_uri)
mlflow.log_artifact(str(art_dir), "parent/and_child")
assert os.listdir(os.path.join(run_artifact_dir, "parent", "and_child")) == \
[os.path.basename(str(art_dir))]
assert os.listdir(os.path.join(run_artifact_dir,
"parent", "and_child",
os.path.basename(str(art_dir)))) == \
[os.path.basename(str(sub_dir))]
示例6: test_get_artifact_uri_with_artifact_path_unspecified_returns_artifact_root_dir
# 需要导入模块: import mlflow [as 别名]
# 或者: from mlflow import get_artifact_uri [as 别名]
def test_get_artifact_uri_with_artifact_path_unspecified_returns_artifact_root_dir():
with mlflow.start_run() as active_run:
assert mlflow.get_artifact_uri(artifact_path=None) == active_run.info.artifact_uri
示例7: test_get_artifact_uri_uses_currently_active_run_id
# 需要导入模块: import mlflow [as 别名]
# 或者: from mlflow import get_artifact_uri [as 别名]
def test_get_artifact_uri_uses_currently_active_run_id():
artifact_path = "artifact"
with mlflow.start_run() as active_run:
assert mlflow.get_artifact_uri(artifact_path=artifact_path) == \
tracking.artifact_utils.get_artifact_uri(
run_id=active_run.info.run_id, artifact_path=artifact_path)
示例8: test_get_artifact_uri_appends_to_uri_path_component_correctly
# 需要导入模块: import mlflow [as 别名]
# 或者: from mlflow import get_artifact_uri [as 别名]
def test_get_artifact_uri_appends_to_uri_path_component_correctly(
artifact_location, expected_uri_format):
client = MlflowClient()
client.create_experiment("get-artifact-uri-test", artifact_location=artifact_location)
mlflow.set_experiment("get-artifact-uri-test")
with mlflow.start_run():
run_id = mlflow.active_run().info.run_id
for artifact_path in ["path/to/artifact", "/artifact/path", "arty.txt"]:
artifact_uri = mlflow.get_artifact_uri(artifact_path)
assert artifact_uri == tracking.artifact_utils.get_artifact_uri(run_id, artifact_path)
assert artifact_uri == expected_uri_format.format(
run_id=run_id, path=artifact_path.lstrip("/"))
示例9: _mlflow_get_output_path
# 需要导入模块: import mlflow [as 别名]
# 或者: from mlflow import get_artifact_uri [as 别名]
def _mlflow_get_output_path():
return mlflow.get_artifact_uri()
示例10: log
# 需要导入模块: import mlflow [as 别名]
# 或者: from mlflow import get_artifact_uri [as 别名]
def log(cls, artifact_path, flavor, registered_model_name=None, **kwargs):
"""
Log model using supplied flavor module. If no run is active, this method will create a new
active run.
:param artifact_path: Run relative path identifying the model.
:param flavor: Flavor module to save the model with. The module must have
the ``save_model`` function that will persist the model as a valid
MLflow model.
:param registered_model_name: (Experimental) If given, create a model version under
``registered_model_name``, also creating a registered model if
one with the given name does not exist.
:param signature: (Experimental) :py:class:`ModelSignature` describes model input
and output :py:class:`Schema <mlflow.types.Schema>`. The model signature
can be :py:func:`inferred <infer_signature>` from datasets representing
valid model input (e.g. the training dataset) and valid model output
(e.g. model predictions generated on the training dataset), for example:
.. code-block:: python
from mlflow.models.signature import infer_signature
train = df.drop_column("target_label")
signature = infer_signature(train, model.predict(train))
:param input_example: (Experimental) Input example provides one or several examples of
valid model input. The example can be used as a hint of what data to
feed the model. The given example will be converted to a Pandas
DataFrame and then serialized to json using the Pandas split-oriented
format. Bytes are base64-encoded.
:param kwargs: Extra args passed to the model flavor.
"""
with TempDir() as tmp:
local_path = tmp.path("model")
run_id = mlflow.tracking.fluent._get_or_start_run().info.run_id
mlflow_model = cls(artifact_path=artifact_path, run_id=run_id)
flavor.save_model(path=local_path, mlflow_model=mlflow_model,
**kwargs)
mlflow.tracking.fluent.log_artifacts(local_path, artifact_path)
try:
mlflow.tracking.fluent._record_logged_model(mlflow_model)
except MlflowException:
# We need to swallow all mlflow exceptions to maintain backwards compatibility with
# older tracking servers. Only print out a warning for now.
_logger.warning(
"Logging model metadata to the tracking server has failed, possibly due older "
"server version. The model artifacts have been logged successfully under %s. "
"In addition to exporting model artifacts, MLflow clients 1.7.0 and above "
"attempt to record model metadata to the tracking store. If logging to a "
"mlflow server via REST, consider upgrading the server version to MLflow "
"1.7.0 or above.", mlflow.get_artifact_uri())
if registered_model_name is not None:
run_id = mlflow.tracking.fluent.active_run().info.run_id
mlflow.register_model("runs:/%s/%s" % (run_id, artifact_path),
registered_model_name)
示例11: main
# 需要导入模块: import mlflow [as 别名]
# 或者: from mlflow import get_artifact_uri [as 别名]
def main(argv):
with mlflow.start_run():
args = parser.parse_args(argv[1:])
# Builds, trains and evaluates a tf.estimator. Then, exports it for inference,
# logs the exported model with MLflow, and loads the fitted model back as a PyFunc.
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.boston_housing.load_data()
# There are 13 features we are using for inference.
feat_cols = [tf.feature_column.numeric_column(key="features", shape=(x_train.shape[1],))]
feat_spec = {
"features": tf.placeholder("float", name="features", shape=[None, x_train.shape[1]])}
hidden_units = [50, 20]
steps = args.steps
regressor = tf.estimator.DNNRegressor(hidden_units=hidden_units, feature_columns=feat_cols)
train_input_fn = tf.estimator.inputs.numpy_input_fn({"features": x_train}, y_train,
num_epochs=None, shuffle=True)
regressor.train(train_input_fn, steps=steps)
test_input_fn = tf.estimator.inputs.numpy_input_fn({"features": x_test}, y_test,
num_epochs=None, shuffle=True)
# Compute mean squared error
mse = regressor.evaluate(test_input_fn, steps=steps)
# Building a receiver function for exporting
receiver_fn = tf.estimator.export.build_raw_serving_input_receiver_fn(feat_spec)
temp = tempfile.mkdtemp()
try:
# The model is automatically logged when export_saved_model() is called.
saved_estimator_path = regressor.export_savedmodel(temp, receiver_fn).decode("utf-8")
# Since the model was automatically logged as an artifact (more specifically
# a MLflow Model), we don't need to use saved_estimator_path to load back the model.
# MLflow takes care of it!
pyfunc_model = pyfunc.load_model(mlflow.get_artifact_uri('model'))
df = pd.DataFrame(data=x_test, columns=["features"] * x_train.shape[1])
# Checking the PyFunc's predictions are the same as the original model's predictions.
predict_df = pyfunc_model.predict(df)
predict_df['original_labels'] = y_test
print(predict_df)
finally:
shutil.rmtree(temp)